CACHE> loading loos_task from cache/vars/tms_analyses_loos_task.RData
loos_task$diffs
mod_selection_res <- as.data.frame(loos_task$diffs) %>% select(c(elpd_diff, se_diff))
models_elpd_diff <- c(
"BV x AE + probe + block",
"BV x AE + probe + block + condition",
"BV x AE + probe + block + condition + randomization",
"BV x AE + probe",
"BV x AE"
)
mod_selection_res <- data.frame(models_elpd_diff, mod_selection_res$elpd_diff, mod_selection_res$se_diff)
names(mod_selection_res) <- c("Model", "elpd_diff", "se_diff")
mod_selection_res = mod_selection_res %>%
mutate(across(where(is.numeric), ~comma(., accuracy=0.01))) %>%
rename_all(~gsub("\\.", " ", .))
alignment= map_chr(mod_selection_res, ~ifelse(class(.x)=="numeric", "r","l"))
elpd_diff se_diff
mod_task02 0.0 0.0
mod_task03 -0.2 1.3
mod_task04 -0.3 1.3
mod_task01 -13.6 5.4
mod_task00 -17.3 6.3
mod_selection_res %>%
kbl(caption="LOO-CV: MW", align=alignment) %>%
kable_classic(full_width=TRUE, html_font="Times") %>%
row_spec(2, bold = TRUE, color = "red")
| Model | elpd_diff | se_diff |
|---|---|---|
| BV x AE + probe + block | 0.00 | 0.00 |
| BV x AE + probe + block + condition | -0.24 | 1.25 |
| BV x AE + probe + block + condition + randomization | -0.29 | 1.26 |
| BV x AE + probe | -13.58 | 5.44 |
| BV x AE | -17.27 | 6.33 |
CACHE> loading mod_task03 from cache/vars/tms_analyses_mod_task03.RData
Scale for 'y' is already present. Adding another scale for 'y', which will
replace the existing scale.
Fig. 1. Model 7: On-task Score: coefficients for all parameters of interest.
| Hypothesis | Estimate | Est Error | CI Lower | CI Upper | Evid Ratio | Post Prob |
|---|---|---|---|---|---|---|
| BV < 0 | -0.03 | 0.05 | -0.13 | 0.06 | 3.37 | 0.77 |
| AE > 0 | 0.06 | 0.05 | -0.03 | 0.15 | 9.58 | 0.91 |
| Block < 0 | -0.11 | 0.02 | -0.15 | -0.07 | Inf | 1.00 |
| Probe number < 0 | -0.04 | 0.01 | -0.07 | -0.02 | 713.29 | 1.00 |
| BV x AE< 0 | -0.04 | 0.05 | -0.14 | 0.06 | 3.75 | 0.79 |
| Active rhTMS > 0 | 0.28 | 0.26 | -0.23 | 0.79 | 6.52 | 0.87 |
| Sham rhTMS > 0 | -0.23 | 0.26 | -0.75 | 0.26 | 0.22 | 0.18 |
| Active arrhTMS > 0 | 0.06 | 0.26 | -0.44 | 0.58 | 1.43 | 0.59 |
| Sham arrhTMS > 0 | -0.32 | 0.26 | -0.84 | 0.19 | 0.12 | 0.11 |
Fig. 2. MW Mean ± 1SE across conditions.
Fig. 3. Mean ± 1SE changes of AE and BV across conditions.
Fig. 4. AE & BV versus On-task score: Mean ± 1SE across conditions. Our subjects are either bad at tapping with the metronome or misunderstood this part of the task.
Fig. 5. AE & BV versus On-task score: BV x AE interaction versus on-off task states. acrive rhTMS seems to flip the interaction: subjects have worse performance when they think they are on task and vice-versa.
CACHE> loading loos_bv from cache/vars/tms_analyses_loos_bv.RData
loos_bv
Output of model 'mod_bv00':
Computed from 10000 by 880 log-likelihood matrix
Estimate SE
elpd_loo -754.0 37.1
p_loo 38.8 1.1
looic 1508.1 74.2
------
Monte Carlo SE of elpd_loo is 0.1.
All Pareto k estimates are good (k < 0.5).
See help('pareto-k-diagnostic') for details.
Output of model 'mod_bv01':
Computed from 10000 by 880 log-likelihood matrix
Estimate SE
elpd_loo -751.8 37.0
p_loo 40.3 1.1
looic 1503.5 74.1
------
Monte Carlo SE of elpd_loo is 0.1.
All Pareto k estimates are good (k < 0.5).
See help('pareto-k-diagnostic') for details.
Output of model 'mod_bv02':
Computed from 10000 by 880 log-likelihood matrix
Estimate SE
elpd_loo -752.8 36.9
p_loo 41.7 1.1
looic 1505.6 73.9
------
Monte Carlo SE of elpd_loo is 0.1.
All Pareto k estimates are good (k < 0.5).
See help('pareto-k-diagnostic') for details.
Output of model 'mod_bv03':
Computed from 10000 by 880 log-likelihood matrix
Estimate SE
elpd_loo -749.5 37.5
p_loo 44.7 1.2
looic 1499.1 75.0
------
Monte Carlo SE of elpd_loo is 0.1.
All Pareto k estimates are good (k < 0.5).
See help('pareto-k-diagnostic') for details.
Model comparisons:
elpd_diff se_diff
mod_bv03 0.0 0.0
mod_bv01 -2.2 4.4
mod_bv02 -3.3 4.0
mod_bv00 -4.5 5.2
as.data.frame(loos_bv$diffs)
elpd_diff se_diff elpd_loo se_elpd_loo p_loo se_p_loo looic
mod_bv03 0.000000 0.000000 -749.5354 37.47914 44.65426 1.245530 1499.071 mod_bv01 -2.226843 4.354602 -751.7623 37.03687 40.26908 1.058170 1503.525 mod_bv02 -3.282269 3.992372 -752.8177 36.94252 41.68978 1.117909 1505.635 mod_bv00 -4.509851 5.198430 -754.0453 37.10229 38.84448 1.067001 1508.091 se_looic mod_bv03 74.95829 mod_bv01 74.07375 mod_bv02 73.88504 mod_bv00 74.20458
loos_bv$diffs
elpd_diff se_diff
mod_bv03 0.0 0.0
mod_bv01 -2.2 4.4
mod_bv02 -3.3 4.0
mod_bv00 -4.5 5.2
mod_selection_res <- as.data.frame(loos_bv$diffs) %>% select(c(elpd_diff, se_diff))
models_elpd_diff <- c(
"probe + block + condition + visit",
"probe + block",
"probe + block + condition",
"probe"
)
mod_selection_res <- data.frame(models_elpd_diff, mod_selection_res$elpd_diff, mod_selection_res$se_diff)
names(mod_selection_res) <- c("Model", "elpd_diff", "se_diff")
mod_selection_res = mod_selection_res %>%
mutate(across(where(is.numeric), ~comma(., accuracy=0.01))) %>%
rename_all(~gsub("\\.", " ", .))
alignment= map_chr(mod_selection_res, ~ifelse(class(.x)=="numeric", "r","l"))
| Model | elpd_diff | se_diff |
|---|---|---|
| probe + block + condition + visit | 0.00 | 0.00 |
| probe + block | -2.23 | 4.35 |
| probe + block + condition | -3.28 | 3.99 |
| probe | -4.51 | 5.20 |
CACHE> loading mod_bv03 from cache/vars/tms_analyses_mod_bv03.RData
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
Fig. 4. Model 7: On-task Score: coefficients for all parameters of interest.
| Hypothesis | Estimate | Est Error | CI Lower | CI Upper | Evid Ratio | Post Prob |
|---|---|---|---|---|---|---|
| Block > 0 | -0.02 | 0.01 | -0.04 | 0.00 | 0.01 | 0.01 |
| Probe number > 0 | 0.00 | 0.01 | -0.01 | 0.01 | 1.17 | 0.54 |
| Active rhTMS > 0 | 0.18 | 0.09 | 0.01 | 0.35 | 50.02 | 0.98 |
| Sham rhTMS > 0 | 0.04 | 0.09 | -0.12 | 0.22 | 2.36 | 0.70 |
| Active arrhTMS > 0 | 0.13 | 0.09 | -0.04 | 0.30 | 13.90 | 0.93 |
| Sham arrhTMS > 0 | 0.04 | 0.08 | -0.12 | 0.21 | 2.21 | 0.69 |
loos_apen=load.cache.var("loos_apen", base=bname)
loos_apen
CACHE> loading loos_apen from cache/vars/tms_analyses_loos_apen.RData
Output of model 'mod_apen00':
Computed from 10000 by 880 log-likelihood matrix
Estimate SE
elpd_loo -1053.1 26.8
p_loo 31.2 1.8
looic 2106.1 53.5
------
Monte Carlo SE of elpd_loo is 0.1.
All Pareto k estimates are good (k < 0.5).
See help('pareto-k-diagnostic') for details.
Output of model 'mod_apen01':
Computed from 10000 by 880 log-likelihood matrix
Estimate SE
elpd_loo -1052.3 26.7
p_loo 32.1 1.9
looic 2104.6 53.5
------
Monte Carlo SE of elpd_loo is 0.1.
Pareto k diagnostic values:
Count Pct. Min. n_eff
(-Inf, 0.5] (good) 879 99.9% 3210
(0.5, 0.7] (ok) 1 0.1% 757
(0.7, 1] (bad) 0 0.0% <NA>
(1, Inf) (very bad) 0 0.0% <NA>
All Pareto k estimates are ok (k < 0.7).
See help('pareto-k-diagnostic') for details.
Output of model 'mod_apen02':
Computed from 10000 by 880 log-likelihood matrix
Estimate SE
elpd_loo -1053.3 26.8
p_loo 32.5 2.0
looic 2106.5 53.6
------
Monte Carlo SE of elpd_loo is NA.
Pareto k diagnostic values:
Count Pct. Min. n_eff
(-Inf, 0.5] (good) 878 99.8% 4808
(0.5, 0.7] (ok) 1 0.1% 1443
(0.7, 1] (bad) 1 0.1% 224
(1, Inf) (very bad) 0 0.0% <NA>
See help('pareto-k-diagnostic') for details.
Output of model 'mod_apen03':
Computed from 10000 by 880 log-likelihood matrix
Estimate SE
elpd_loo -1053.7 26.8
p_loo 33.5 1.9
looic 2107.5 53.5
------
Monte Carlo SE of elpd_loo is 0.1.
Pareto k diagnostic values:
Count Pct. Min. n_eff
(-Inf, 0.5] (good) 879 99.9% 2730
(0.5, 0.7] (ok) 1 0.1% 732
(0.7, 1] (bad) 0 0.0% <NA>
(1, Inf) (very bad) 0 0.0% <NA>
All Pareto k estimates are ok (k < 0.7).
See help('pareto-k-diagnostic') for details.
Model comparisons:
elpd_diff se_diff
mod_apen01 0.0 0.0
mod_apen00 -0.8 2.0
mod_apen02 -1.0 2.0
mod_apen03 -1.4 2.0
as.data.frame(loos_apen$diffs)
elpd_diff se_diff elpd_loo se_elpd_loo p_loo se_p_loo looic
mod_apen01 0.0000000 0.000000 -1052.285 26.72867 32.12331 1.859763 2104.570 mod_apen00 -0.7681244 1.986805 -1053.053 26.76322 31.22394 1.841387 2106.106 mod_apen02 -0.9709216 1.967294 -1053.256 26.82159 32.47110 1.988178 2106.512 mod_apen03 -1.4497045 2.012275 -1053.735 26.75017 33.49944 1.923370 2107.469 se_looic mod_apen01 53.45735 mod_apen00 53.52645 mod_apen02 53.64319 mod_apen03 53.50033
loos_apen$diffs
elpd_diff se_diff
mod_apen01 0.0 0.0
mod_apen00 -0.8 2.0
mod_apen02 -1.0 2.0
mod_apen03 -1.4 2.0
mod_selection_res <- as.data.frame(loos_apen$diffs) %>% select(c(elpd_diff, se_diff))
models_elpd_diff <- c(
"probe + block",
"probe",
"probe + block + condition",
"probe + block + condition + visit"
)
mod_selection_res <- data.frame(models_elpd_diff, mod_selection_res$elpd_diff, mod_selection_res$se_diff)
names(mod_selection_res) <- c("Model", "elpd_diff", "se_diff")
mod_selection_res = mod_selection_res %>%
mutate(across(where(is.numeric), ~comma(., accuracy=0.01))) %>%
rename_all(~gsub("\\.", " ", .))
alignment= map_chr(mod_selection_res, ~ifelse(class(.x)=="numeric", "r","l"))
| Model | elpd_diff | se_diff |
|---|---|---|
| probe + block | 0.00 | 0.00 |
| probe | -0.77 | 1.99 |
| probe + block + condition | -0.97 | 1.97 |
| probe + block + condition + visit | -1.45 | 2.01 |
CACHE> loading mod_apen03 from cache/vars/tms_analyses_mod_apen03.RData
## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.
Fig. 4. Model 7: On-task Score: coefficients for all parameters of interest.
| Hypothesis | Estimate | Est Error | CI Lower | CI Upper | Evid Ratio | Post Prob |
|---|---|---|---|---|---|---|
| Block < 0 | -0.02 | 0.01 | -0.05 | 0.00 | 23.81 | 0.96 |
| Probe number < 0 | 0.02 | 0.01 | 0.00 | 0.04 | 0.01 | 0.01 |
| Active rhTMS < 0 | -0.27 | 0.12 | -0.50 | -0.04 | 83.03 | 0.99 |
| Sham rhTMS < 0 | -0.22 | 0.12 | -0.45 | 0.01 | 32.78 | 0.97 |
| Active arrhTMS < 0 | -0.23 | 0.12 | -0.47 | -0.01 | 43.25 | 0.98 |
| Sham arrhTMS < 0 | -0.12 | 0.12 | -0.35 | 0.11 | 5.97 | 0.86 |
### Non-parametric ANOVA (Kruskal-Wallis rank sum test)
shapiro.test(tms_data.nback$probe.response)
shapiro.test(tms_data.nback$zlog.apen)
shapiro.test(tms_data.nback$bv)
Shapiro-Wilk normality test
data: tms_data.nback$probe.response
W = 0.87298, p-value < 2.2e-16
Shapiro-Wilk normality test
data: tms_data.nback$zlog.apen
W = 0.9406, p-value < 2.2e-16
Shapiro-Wilk normality test
data: tms_data.nback$bv
W = 0.5741, p-value < 2.2e-16
Distributions are non-normal
kruskal.test(probe.response ~ condition, data = tms_data.nback) # groups are different
Kruskal-Wallis rank sum test
data: probe.response by condition
Kruskal-Wallis chi-squared = 15.76, df = 4, p-value = 0.003359
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
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* NA -> ...1
* NA -> ...2
* NA -> ...3
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* NA -> ...1
* NA -> ...2
* NA -> ...3
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* NA -> ...1
* NA -> ...2
* NA -> ...3
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* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
pairwise_wilcox_results %>%
kbl(caption="Pairwise Wilcoxon test: MW", align=alignment) %>%
kable_classic(full_width=TRUE, html_font="Times") %>%
row_spec(c(3, 4, 6, 7), bold = TRUE) %>%
row_spec(1, color = "red")
| Comparison | W-statistic | p-value | Effect size, r |
|---|---|---|---|
| baseline vs. active rhTMS | 18312 | 0.41 | 0.04 |
| baseline vs. active arrhTMS | 20483 | 0.23 | 0.06 |
| baseline vs. sham rhTMS | 21615 | 0.02 | 0.11 |
| baseline vs. sham arrhTMS | 22196 | 0.00 | 0.14 |
| active rhTMS vs. active arrhTMS | 14191 | 0.07 | 0.10 |
| active rhTMS vs. sham rhTMS | 14899 | 0.01 | 0.15 |
| active rhTMS vs. sham arrhTMS | 15308 | 0.00 | 0.18 |
| active arrhTMS vs. sham rhTMS | 13626 | 0.29 | 0.06 |
| active arrhTMS vs. sham arrhTMS | 13944 | 0.14 | 0.08 |
| sham rhTMS vs. sham arrhTMS | 13088 | 0.71 | 0.02 |
kruskal.test(zlog.apen ~ condition, data = tms_data.nback) # groups are different
Kruskal-Wallis rank sum test
data: zlog.apen by condition
Kruskal-Wallis chi-squared = 9.6253, df = 4, p-value = 0.04724
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
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* NA -> ...1
* NA -> ...2
* NA -> ...3
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* NA -> ...1
* NA -> ...2
* NA -> ...3
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* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
New names:
* NA -> ...1
* NA -> ...2
* NA -> ...3
pairwise_wilcox_results %>%
kbl(caption="Pairwise Wilcoxon test: AE", align=alignment) %>%
kable_classic(full_width=TRUE, html_font="Times") %>%
row_spec(c(1, 2), bold = TRUE) %>%
row_spec(1, color = "red")
| Comparison | W-statistic | p-value | Effect size, r |
|---|---|---|---|
| baseline vs. active rhTMS | 22138.5 | 0.01 | 0.13 |
| baseline vs. active arrhTMS | 21689.0 | 0.03 | 0.11 |
| baseline vs. sham rhTMS | 21311.5 | 0.06 | 0.09 |
| baseline vs. sham arrhTMS | 19889.5 | 0.54 | 0.03 |
| active rhTMS vs. active arrhTMS | 12561.5 | 0.77 | 0.02 |
| active rhTMS vs. sham rhTMS | 12431.0 | 0.66 | 0.02 |
| active rhTMS vs. sham arrhTMS | 11337.5 | 0.08 | 0.10 |
| active arrhTMS vs. sham rhTMS | 12673.5 | 0.88 | 0.01 |
| active arrhTMS vs. sham arrhTMS | 11567.5 | 0.14 | 0.08 |
| sham rhTMS vs. sham arrhTMS | 11767.5 | 0.21 | 0.07 |
pairwise_wilcox_results <- data.frame(matrix(ncol = 3, nrow = 10))
colnames(pairwise_wilcox_results) <- c("Comparison", "W-statistic", "p-value")
pairwise_wilcox_results$Comparison <- tms_comparison_list
i = 1
for (comparison in tms_comparison_list) {
r <- wilcox.test(filter(tms_data.nback, condition == comparison[1])$zbv, filter(tms_data.nback, condition == comparison[2])$zbv, paired = FALSE)
pairwise_wilcox_results[i, 2:3] <- rbind( bind_cols(r$statistic, r$p.value))
i = i + 1
}
New names:
* NA -> ...1
* NA -> ...2
New names:
* NA -> ...1
* NA -> ...2
New names:
* NA -> ...1
* NA -> ...2
New names:
* NA -> ...1
* NA -> ...2
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* NA -> ...1
* NA -> ...2
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* NA -> ...1
* NA -> ...2
New names:
* NA -> ...1
* NA -> ...2
New names:
* NA -> ...1
* NA -> ...2
New names:
* NA -> ...1
* NA -> ...2
New names:
* NA -> ...1
* NA -> ...2
pairwise_wilcox_results <- pairwise_wilcox_results %>%
mutate(across("p-value", ~comma(., accuracy=0.01)), Comparison = comparisons) %>%
rename_all(~gsub("\\.", " ", .))
pairwise_wilcox_results %>%
kbl(caption="Pairwise Wilcoxon test: BV", align=alignment) %>%
kable_classic(full_width=TRUE, html_font="Times") %>%
row_spec(c(2), bold = TRUE) %>%
row_spec(1, color = "red")
| Comparison | W-statistic | p-value |
|---|---|---|
| baseline vs. active rhTMS | 16467 | 0.02 |
| baseline vs. active arrhTMS | 16813 | 0.04 |
| baseline vs. sham rhTMS | 18209 | 0.38 |
| baseline vs. sham arrhTMS | 17964 | 0.28 |
| active rhTMS vs. active arrhTMS | 13154 | 0.67 |
| active rhTMS vs. sham rhTMS | 13859 | 0.20 |
| active rhTMS vs. sham arrhTMS | 13786 | 0.23 |
| active arrhTMS vs. sham rhTMS | 13625 | 0.32 |
| active arrhTMS vs. sham arrhTMS | 13615 | 0.32 |
| sham rhTMS vs. sham arrhTMS | 12678 | 0.88 |
wilcox.test(filter(tms_data.nback_z, (`Condition` == "baseline" & `Measure` == "zlog.apen"))$`Z-score`,
filter(tms_data.nback_z, (`Condition` == "active_rhTMS" & `Measure` == "zlog.apen"))$`Z-score`,
paired = FALSE)
rstatix::wilcox_effsize(tms_data.nback, zlog.apen ~ condition, paired = FALSE)
Wilcoxon rank sum test with continuity correction
data: filter(tms_data.nback_z, (Condition == "baseline" & Measure == "zlog.apen"))$`Z-score` and filter(tms_data.nback_z, (Condition == "active_rhTMS" & Measure == "zlog.apen"))$`Z-score`
W = 22138, p-value = 0.009497
alternative hypothesis: true location shift is not equal to 0
# A tibble: 10 x 7
.y. group1 group2 effsize n1 n2 magnitude
* <chr> <chr> <chr> <dbl> <int> <int> <ord>
1 zlog.apen baseline active_rhTMS 0.130 240 160 small
2 zlog.apen baseline active_arrhTMS 0.110 240 160 small
3 zlog.apen baseline sham_rhTMS 0.0932 240 160 small
4 zlog.apen baseline sham_arrhTMS 0.0304 240 160 small
5 zlog.apen active_rhTMS active_arrhTMS 0.0161 160 160 small
6 zlog.apen active_rhTMS sham_rhTMS 0.0249 160 160 small
7 zlog.apen active_rhTMS sham_arrhTMS 0.0988 160 160 small
8 zlog.apen active_arrhTMS sham_rhTMS 0.00855 160 160 small
9 zlog.apen active_arrhTMS sham_arrhTMS 0.0833 160 160 small
10 zlog.apen sham_rhTMS sham_arrhTMS 0.0697 160 160 small
kruskal.test(zbv ~ condition, data = tms_data.nback) # groups are NOT different
tms_data.nback$bv_ranks <- rank(tms_data.nback$zbv)
by(tms_data.nback$bv_ranks, tms_data.nback$condition, mean) # where the difference lies
pgirmess::kruskalmc(zbv ~ condition, data = tms_data.nback, cont = "two-tailed")
pgirmess::kruskalmc(zbv ~ condition, data = tms_data.nback)
Kruskal-Wallis rank sum test
data: zbv by condition
Kruskal-Wallis chi-squared = 7.4528, df = 4, p-value = 0.1138
tms_data.nback$condition: baseline
[1] 409.8875
------------------------------------------------------------
tms_data.nback$condition: active_rhTMS
[1] 472.575
------------------------------------------------------------
tms_data.nback$condition: active_arrhTMS
[1] 463.4563
------------------------------------------------------------
tms_data.nback$condition: sham_rhTMS
[1] 434.1562
------------------------------------------------------------
tms_data.nback$condition: sham_arrhTMS
[1] 437.7312
Multiple comparison test after Kruskal-Wallis, treatments vs control (two-tailed)
p.value: 0.05
Comparisons
obs.dif critical.dif difference
baseline-active_rhTMS 62.68750 64.79542 FALSE
baseline-active_arrhTMS 53.56875 64.79542 FALSE
baseline-sham_rhTMS 24.26875 64.79542 FALSE
baseline-sham_arrhTMS 27.84375 64.79542 FALSE
Multiple comparison test after Kruskal-Wallis
p.value: 0.05
Comparisons
obs.dif critical.dif difference
baseline-active_rhTMS 62.68750 72.82000 FALSE
baseline-active_arrhTMS 53.56875 72.82000 FALSE
baseline-sham_rhTMS 24.26875 72.82000 FALSE
baseline-sham_arrhTMS 27.84375 72.82000 FALSE
active_rhTMS-active_arrhTMS 9.11875 79.77032 FALSE
active_rhTMS-sham_rhTMS 38.41875 79.77032 FALSE
active_rhTMS-sham_arrhTMS 34.84375 79.77032 FALSE
active_arrhTMS-sham_rhTMS 29.30000 79.77032 FALSE
active_arrhTMS-sham_arrhTMS 25.72500 79.77032 FALSE
sham_rhTMS-sham_arrhTMS 3.57500 79.77032 FALSE
AE and BV distributions
AE and BV distributions
tms_data.nback_z %>%
ggplot(aes(x= `On-task Score`, y =`Z-score`, group = `Measure`, color = `Measure`)) +
geom_pointrange(stat="summary", fun.data=mean_se, fun.args = list(mult=1), position=position_dodge(0.05)) +
geom_line(stat="summary", fun.data=mean_se, fun.args = list(mult=2)) +
facet_wrap(~`Condition`) +
scale_color_manual(labels = c("AE", "BV"), values = c("blue", "red")) +
theme(text = element_text(size = 15))
tms_data.nback_z %>%
ggplot(aes(x= `Focus`, y =`Z-score`, group = `Measure`, color=`Measure`)) +
geom_pointrange(stat="summary", fun.data=mean_se, fun.args = list(mult=1), position=position_dodge(0.05)) +
geom_line(stat="summary", fun.data=mean_se, fun.args = list(mult=2)) +
scale_color_manual(labels = c("AE", "BV"), values = c("blue", "red")) +
facet_wrap(~ `Condition`)+
theme(text = element_text(size = 18))